Spatial and Spectral features for Horticulture mapping

Autores
Marinelli, María Victoria; Mari, Nicolás Alejandro; Pons, Diego Hernan; Giobellina, Beatriz Liliana; Scavuzzo, Carlos Marcelo
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one.
EEA Manfredi
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Marinelli, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Cruz del Eje; Argentina
Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
Fuente
Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 37-40
Materia
Áreas Periurbanas
Cultivo de Hortalizas
Alimentación Humana
Periurban Areas
Vegetable Growing
Human Feeding
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/19388
Acceder
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oai_identifier_str oai:localhost:20.500.12123/19388
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spelling Spatial and Spectral features for Horticulture mappingMarinelli, María VictoriaMari, Nicolás AlejandroPons, Diego HernanGiobellina, Beatriz LilianaScavuzzo, Carlos MarceloÁreas PeriurbanasCultivo de HortalizasAlimentación HumanaPeriurban AreasVegetable GrowingHuman FeedingRemote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one.EEA ManfrediFil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; ArgentinaFil: Marinelli, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); ArgentinaFil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Cruz del Eje; ArgentinaFil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); ArgentinaFil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; ArgentinaFil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); ArgentinaFil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); ArgentinaUniversidad Técnica Federico Santa María, Chile2024-09-13T13:04:31Z2024-09-13T13:04:31Z2019-09-25info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/20.500.12123/19388978-956-356-095-4 (Online)Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 37-40reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-11T10:25:16Zoai:localhost:20.500.12123/19388instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-11 10:25:16.959INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Spatial and Spectral features for Horticulture mapping
title Spatial and Spectral features for Horticulture mapping
spellingShingle Spatial and Spectral features for Horticulture mapping
Marinelli, María Victoria
Áreas Periurbanas
Cultivo de Hortalizas
Alimentación Humana
Periurban Areas
Vegetable Growing
Human Feeding
title_short Spatial and Spectral features for Horticulture mapping
title_full Spatial and Spectral features for Horticulture mapping
title_fullStr Spatial and Spectral features for Horticulture mapping
title_full_unstemmed Spatial and Spectral features for Horticulture mapping
title_sort Spatial and Spectral features for Horticulture mapping
dc.creator.none.fl_str_mv Marinelli, María Victoria
Mari, Nicolás Alejandro
Pons, Diego Hernan
Giobellina, Beatriz Liliana
Scavuzzo, Carlos Marcelo
author Marinelli, María Victoria
author_facet Marinelli, María Victoria
Mari, Nicolás Alejandro
Pons, Diego Hernan
Giobellina, Beatriz Liliana
Scavuzzo, Carlos Marcelo
author_role author
author2 Mari, Nicolás Alejandro
Pons, Diego Hernan
Giobellina, Beatriz Liliana
Scavuzzo, Carlos Marcelo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Áreas Periurbanas
Cultivo de Hortalizas
Alimentación Humana
Periurban Areas
Vegetable Growing
Human Feeding
topic Áreas Periurbanas
Cultivo de Hortalizas
Alimentación Humana
Periurban Areas
Vegetable Growing
Human Feeding
dc.description.none.fl_txt_mv Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one.
EEA Manfredi
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Marinelli, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Marinelli, María Victoria. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Cruz del Eje; Argentina
Fil: Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina
Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia de Extensión Rural Córdoba; Argentina
Fil: Giobellina, Beatriz Liliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Observatorio de Agricultura Urbana, Periurbana y Agroecología (O-AUPA); Argentina
Fil: Scavuzzo, Carlos Marcelo. Universidad Nacional de Córdoba. Instituto de Estudios Espaciales Avanzados Mario Gulich (IG). Comisión Nacional de Actividades Espaciales (CONAE); Argentina
description Remote sensing data allows the continuous mapping of horticultural crops in periurban areas. These are very important for their functions in the provision of local food and for other ecosystem services they provide. This work presents a methodological development and the results of a hierarchical classification of the horticultural periurban area of Cordoba city, based on the spectral and spatial properties of satellite imagery. The methodology present is automatable, making it suitable for continuous monitoring. The classification obtained with the RF algorithm yields a global kappa of 0.77 and in particular for the horticultural class a precision of 0.82. With a hierarchical classification only of the horticultural area result in an amount of 1860 ha. With spectral information taken in radiometer fields campaigns evaluated by spectral angle mapper, we can observe as using Sentinel 2 spectra and parrot camera produce better separability of horticultural crops that the hyperspectral one.
publishDate 2019
dc.date.none.fl_str_mv 2019-09-25
2024-09-13T13:04:31Z
2024-09-13T13:04:31Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/19388
978-956-356-095-4 (Online)
url http://hdl.handle.net/20.500.12123/19388
identifier_str_mv 978-956-356-095-4 (Online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Técnica Federico Santa María, Chile
publisher.none.fl_str_mv Universidad Técnica Federico Santa María, Chile
dc.source.none.fl_str_mv Proceedings of BigDSSAgro 2019. III International Conference on Agro BigData and Decision Support Systems in Agriculture. 25-27 September 2019, Valparaíso, Chile. p. 37-40
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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